AI, prescriptive maintenance, and edge analytics are reshaping how process plants run. Yet the gap between a successful pilot and full-scale impact rarely closes on technology alone. The differentiators are culture, operating model, and data foundations.
Build versus partner: keep the core, rent the niche
- Keep in-house: architecture, data governance, cybersecurity, product ownership, and integration with MES/ERP/CMMS. These are enduring capabilities that define how your enterprise runs.
- Partner for specialization: anomaly detection, prescriptive models, and industry-specific reliability libraries (e.g., with providers such as Infinite Uptime). Niche models and tooling evolve quickly and are costly to sustain internally.
- Decide on total cost of ownership: include model upkeep, data labeling, platform upgrades, skills churn, and support coverage—not just license or project fees.
Realistic ROI: align timelines with culture and sponsorship
- Pilot (proof): 6–12 weeks is feasible when data access is clear and a plant team is engaged.
- Stabilize: 1–3 months to tune alerts, embed workflows, and validate savings against a baseline.
- Scale: 6–18 months across multiple sites, depending on executive sponsorship, local champions, and data readiness.
- What accelerates results:
- Clear executive mandate and funding guardrails
- Standard playbook for onboarding new assets and sites
- Shared KPIs measured from day one (unplanned downtime, MTBF, maintenance backlog, energy intensity)
- A federated change network: central platform team plus plant champions
Non-technical factors matter more than tools
- Workforce profile: long-tenured teams are skilled but “hardwired” by past methods. Design for unlearning as much as new learning.
- Trust in data: start with transparent, explainable alerts tied to familiar engineering limits before introducing complex models.
- Influencers: identify respected operators and technicians; make them co-owners of the change.
- Language and culture: communicate wins in local terms and practical outcomes, not vendor jargon.
Scaling across plants: standardize the spine, localize the muscle
- Shared platform, standards, and security policies to avoid fragmentation.
- Local adaptations for utilities, raw materials, SOPs, and regulatory context.
- Time-zone-aware support and escalation paths.
- Repeatable onboarding sequence: data access, asset criticality, signal mapping, thresholds, pilot assets, playbook handover.
Beyond uptime: use data to improve decisions, not just alarms
- Contextualize and harmonize: combine OT signals (process, vibration, temperature), maintenance history, quality events, and cost data. Single-source signals rarely explain plant-wide outcomes.
- Edge plus cloud: keep critical detection at the edge for speed and resilience; manage fleet learning and benchmarking in the cloud.
- Extend use cases:
- Energy and steam optimization
- Startup and shutdown risk reduction
- Yield stabilization through variance detection
- Safer operations through early detection of misconfiguration
What “AI for production outcomes” should mean
- Efficiency gains across people and processes: fewer blind handovers, faster triage, better decisions with the same headcount.
- Tangible results: fewer failures, shorter downtime, lower energy per ton, reduced scrap, safer restarts.
Start with culture and governance, choose where to build versus buy, invest in data context, and scale through a repeatable playbook. Technology will perform; your operating model determines whether it performs everywhere.
Source: Insights from Rajneesh Ojha, Head of Digital Transformation, Indorama Ventures, at the CXO Circle event, Bangkok. Collaboration example: Infinite Uptime (prescriptive maintenance and reliability analytics).
The trip to Thailand was sponsored by InfiniteUptime.
About the author
Lucian Fogoros is the Co-founder of IIoT World.